A
global-through-urban scale nested and coupled air pollution
and weather forecast model was developed and applied to the
SARMAP Field Campaign. The model, GATOR-GCMM (Gas, Aerosol,
Transport, Radiation, General Circulation, and Mesoscale Meteorological)
model, treats nesting of all important air quality (including
size- and composition-resolved aerosol) and meteorological parameters
simultaneously, from the global scale through the urban scale
(<5-km grid spacing). It treats any number of nested layers
and any number of meteorological and air quality grids in each
layer between the global and urban scales. The model does not
use nudging, or data assimilation; hence, it is entirely prognostic,
except for the emissions inventory. It is configured so that,
regardless of the number of grids used during a single continuous
simulation, the total central memory requirement of the model
never exceeds 1.5 times and 2.1 times the central memory requirement
of the largest grid for gas simulations and gas/aerosol simulations,
respectively. The model includes feedbacks of air pollutants
to meteorology. New aerosol numerical algorithms were developed
for the model. New algorithms accounting for subgrid-scale soil
classification and vegetation cover heterogeneity in ground-temperature
calculations were also developed. The ground-temperature module
treats temperatures over sea ice, urban areas, and snow-covered
soil, vegetation, sea-ice, and urban areas. Urban areas are
broken down into road surfaces, rooftops, vegetation, and bare
soil. The model was applied with five global-through-urban scale
nested grids to the August 3-6, 1990 SARMAP field campaign.
Parameters compared with observations included air temperatures,
air pressures, relative humidities, wind speeds/directions,
and the following 20 gases and carbon bond groups: O3, NO, NO2,
CO, SO2, nonmethane organic carbon, CH4, C2H6, C3H8, paraffins,
ethene, olefins, formaldehyde, higher aldehydes, acetone, other
ketones, toluene, xylene, benzaldehdye, and isoprene. In the
absence of nesting, the gross error in near-surface temperatures,
normalized over all 8617 day and night observations in the model
domain, was 1.06% of observed kelvin temperatures. The gross
error in near-surface relative humidity (RH), normalized over
all 5597 day and night observations in the domain was less than
24.8% of observed RH values. The gross error in near-surface
ozone mixing ratio, normalized over all 2866 measurements above
50 ppbv during the 4-day period, was 23.5%. Nesting improved
model statistics for all near-surface parameters, but not significantly,
probably due to the fact that the air pollution episode occurred
under a synoptic high pressure system. Finer-resolution grid
spacing resulted in higher average ozone mixing ratios than
coarser-resolution spacing, except in the largest regional grid;
hence, the coarser the grid, the greater the underprediction
of ozone in most cases.